Jon P. Christophersen

Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.

Comparison of prognostic algorithms for estimating remaining useful life of batteries

The estimation of remaining useful life (RUL) of a faulty component is at the centre of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. RUL prediction needs to contend with multiple sources of errors, like modelling inconsistencies, system noise and degraded sensor fidelity, which leads to unsatisfactory performance from classical techniques like autoregressive integrated moving average (ARIMA) and extended Kalman filtering (EKF). The Bayesian theory of uncertainty management provides a way to contain these problems. The relevance vector machine (RVM), the Bayesian treatment of the well known support vector machine (SVM), a kernel-based regression/classification technique, is used for model development. This model is incorporated into a particle filter (PF) framework, where statistical estimates of noise and anticipated operational conditions are used to provide estimates of RUL in the form of a probability density function (pdf). We present here a comparative study of the above-mentioned approaches on experimental data collected from Li-ion batteries. Batteries were chosen as an example of a complex system whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions.

An integrated approach to battery health monitoring using bayesian regression and state estimation

The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of relevance vector machine (RVM), and to state estimation via particle filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of-life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electrochemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the support vector machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.

Effect of cathode composition on capacity fade, impedance rise and power fade in high-power, lithium-ion cells ☆

We tested the effect of Al concentration on the performance of lithium-ion cells. One set of cells contained a LiNi{sub 0.8}Co{sub 0.15}Al{sub 0.05}O{sub 2} cathode and the other, LiNi{sub 0.8}Co{sub 0.10}Al{sub 0.10}O{sub 2}. The cells were calendar- and cycle-life tested at several temperatures, with periodic interruptions for reference performance tests that were used to gauge capacity and power fade as a function of time. The C{sub 1}/25 capacity fade in the cells displayed t{sup 1/2} dependence. The capacity fade of the 10% Al-doped cells tested at 45 {sup o}C was similar to that of the 5% Al-doped cells at 25 {sup o}C. The impedance rise and power fade were also sensitive to the Al concentration. For the one common temperature investigated (i.e., 45 {sup o}C), the 10% Al-doped cells displayed higher impedance rise and power fade than the 5% Al-doped cells. Additionally, the time dependence of the impedance rise displayed two distinct kinetic regimes; the initial portion depended on t{sup 1/2} and the final, on t. On the other hand, the 10% Al-doped cells depended on t{sup 1/2}2 only.

Advanced Technology Development Program for Lithium-Ion Batteries: Gen 2 Performance Evaluation Final Report

The Advanced Technology Development Program has completed performance testing of the second generation of lithium-ion cells (i.e., Gen 2 cells). The 18650-size Gen 2 cells, with a baseline and variant chemistry, were distributed over a matrix consisting of three states-of-charge (SOCs) (60, 80, and 100% SOC), four temperatures (25, 35, 45, and 55°C), and three life tests (calendar-, cycle-, and accelerated-life). The calendar- and accelerated-life cells were clamped at an open-circuit voltage corresponding to the designated SOC and were subjected to a once-per-day pulse profile. The cycle-life cells were continuously pulsed using a profile that was centered around 60% SOC. Life testing was interrupted every four weeks for reference performance tests (RPTs), which were used to quantify changes in cell degradation as a function of aging. The RPTs generally consisted of C1/1 and C1/25 static capacity tests, a low-current hybrid pulse power characterization test, and electrochemical impedance spectroscopy. The rate of cell degradation generally increased with increasing test temperature, and SOC. It was also usually slowest for the calendar-life cells and fastest for the accelerated-life cells. Detailed capacity-, power-, and impedance-based performance results are reported.

Effects of Reference Performance Testing during Aging Using Commercial Lithium-Ion Cells

The Advanced Technology Development Program, under the oversight of the U.S. Department of Energy’s FreedomCAR and Vehicle Technologies Program, is investigating lithium-ion batteries for hybrid-electric vehicle applications. Cells are aged under various test conditions, including temperatures and states-of-charge. Life testing is interrupted at regular intervals to conduct reference performance tests (RPTs), which are used to measure changes in the electrical performance of the cells and then to determine cell degradation as a function of test time. Although designed to be unobtrusive, data from the Advanced Technology Development Gen 2 cells indicated that RPTs actually contributed to cell degradation and failure. A study was performed at the Idaho National Laboratory using commercially available lithium-ion cells to determine the impact of RPTs on life. A series of partial RPTs were performed at regular intervals during life testing and compared to a control group that was life tested without RPT interruption. It was determined that certain components of the RPT were detrimental, while others appeared to improve cell performance. Consequently, a new “mini” RPT was designed as an unobtrusive alternative. Initial testing with commercial cells indicates that the impact of the mini RPT is significantly less than the Gen 2 cell RPT.

Crosstalk compensation for a rapid, higher-resolution impedance spectrum measurement

Crosstalk Compensation is an approach that enables rapid, higher-resolution impedance spectra measurements of energy storage devices. The input signal consists of a sum-of-sines excitation current that has a known frequency spread. The advantage of Crosstalk Compensation is that high resolution spectra can be acquired within one period of the lowest frequency while also including non-harmonic frequencies. The crosstalk interference at a given frequency can be pre-determined and assigned to an error matrix. The real and imaginary impedance can then be calculated based on the inverse of the error matrix and captured response. Analytical validation of Crosstalk Compensation was performed using a battery equivalent circuit model. Two different frequency ranges were simulated, and both indicated that a minimum step factor between frequencies should be 1.25 to reduce the error in compensating the captured response signal. For a frequency range of 1638.4-0.1 Hz, for example, a maximum of 45 frequencies should be included within the excitation signal to accurately acquire the impedance spectra at high resolution. A simplified derivation of Crosstalk Compensation and its corresponding analytical validation studies are discussed.

Long-Term Validation of Rapid Impedance Spectrum Measurements as a Battery State-of-Health Assessment Technique

CITATION: SAE Int. J. Alt. Power. ____________________________________ EXPERIMENTAL Christophersen et al / SAE Int. J. Alt. Power. / Volume 6, Issue 1(May 2013)

Universal auto-calibration for a rapid battery impedance spectrum measurement device

Electrochemical impedance spectroscopy has been shown to be a valuable tool for diagnostics and prognostics of energy storage devices such as batteries and ultra-capacitors. Although measurements have been typically confined to laboratory environments, rapid impedance spectrum measurement techniques have been developed for on-line, embedded applications as well. The prototype hardware for the rapid technique has been validated using lithium-ion batteries, but issues with calibration had also been identified. A new, universal automatic calibration technique was developed to address the identified issues while also enabling a more simplified approach. A single, broad-frequency range is used to calibrate the system and then scaled to the actual range and conditions used when measuring a device under test. The range used for calibration must be broad relative to the expected measurement conditions for the scaling to be successful. Validation studies were performed by comparing the universal calibration approach with data acquired from targeted calibration ranges based on the expected range of performance for the device under test. First, a mid-level shunt range was used for calibration and used to measure devices with lower and higher impedance. Next, a high-excitation current level was used for calibration, followed by measurements using lower currents. Finally, calibration was performed over a wide frequency range and used to measure test articles with a lower set of frequencies. In all cases, the universal calibration approach compared very well with results acquired following a targeted calibration. Additionally, the shunts used for the automated calibration technique were successfully characterized such that the rapid impedance measurements compare very well with laboratory-scale measurements. These data indicate that the universal approach can be successfully used for onboard rapid impedance spectra measurements for a broad set of test devices and range of measurement conditions.

Battery Calendar Life Estimator Manual Modeling and Simulation

The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.